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Generalization Bounds

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Showing 601–650 of 686 papers

TitleStatusHype
Nonparametric Hawkes Processes: Online Estimation and Generalization Bounds—0
Lifelong Learning by Adjusting Priors—0
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy—0
Empirical Risk Landscape Analysis for Understanding Deep Neural Networks—0
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors—0
Estimating the Success of Unsupervised Image to Image TranslationCode0
How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View—0
On the Discrimination-Generalization Tradeoff in GANs—0
Sparsity, variance and curvature in multi-armed bandits—0
Regularization via Mass TransportationCode0
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data—0
Stability and Generalization of Learning Algorithms that Converge to Global Optima—0
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient DescentCode0
Model-Powered Conditional Independence TestCode0
Boosting the kernelized shapelets: Theory and algorithms for local features—0
A Nonlinear Kernel Support Matrix Machine for Matrix Learning—0
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints—0
PAC-Bayes and Domain Adaptation—0
Data-dependent Generalization Bounds for Multi-class Classification—0
An a Priori Exponential Tail Bound for k-Folds Cross-Validation—0
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional RepresentationsCode0
Nuclear Discrepancy for Active Learning—0
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions—0
Learning from partial correction—0
Minimax Statistical Learning with Wasserstein Distances—0
The Landscape of Deep Learning Algorithms—0
Mass Volume Curves and Anomaly Ranking—0
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training DataCode0
On the benefits of output sparsity for multi-label classification—0
Data-Dependent Stability of Stochastic Gradient Descent—0
A Theory of Output-Side Unsupervised Domain Adaptation—0
Learning Kernels with Random Features—0
Generalization Bounds for Weighted Automata—0
Kernel Ridge Regression via Partitioning—0
Distribution-dependent concentration inequalities for tighter generalization bounds—0
Multi-task and Lifelong Learning of Kernels—0
The Structured Weighted Violations Perceptron Algorithm—0
Loss factorization, weakly supervised learning and label noise robustness—0
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes—0
Foundations of Coupled Nonlinear Dimensionality Reduction—0
A study of the classification of low-dimensional data with supervised manifold learning—0
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms—0
On the accuracy of self-normalized log-linear models—0
Random Maxout Features—0
When and why are log-linear models self-normalizing?—0
Towards a Learning Theory of Cause-Effect InferenceCode0
(Almost) No Label No Cry—0
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning—0
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality—0
Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side KnowledgeCode0
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